1. Field of the Invention
The invention relates to a method for image resolution enhancement and, more particularly, to a method that is based on a fuzzy analysis simulating the human vision system and a technique of neural network to enhance the image resolution.
2. Description of the Related Art
As innovations of technology moves on, the products of digital images such as digital cameras, digital camcorders, projectors, and multifunction office machines have become very popular with the consumers. However, all digital image products have one common technical requirement, which is image resolution adjustment or conversion. Therefore, the enhancement of high-quality image resolution technique has always been an important issue for digital image processing.
The technique of image interpolation is to convert a low-resolution image into a high-resolution image by interpolation. However, most of the image interpolation techniques, such as bilinear interpolation and bi-cubic interpolation, cannot meet the requirements of high-resolution image because the conventional image interpolation techniques always generate two obvious shortcomings, image edge jaggedness and image blurring. Similarly, other conventional interpolation techniques based on the principle of linear interpolation have the same shortcomings as the above two techniques have.
The human vision system is more sensitive to the edge of an image than to the other portion of the image such as a smooth portion or a structure portion. Therefore, when an image interpolation is applied, the image edge will be taken into consideration in particular. In recent years, the concept of image contour has been adopted to cope with the problems brought by image resolution enhancement. For example, an American Published U.S. Pat. No. 5,991,464 filed by Pohsiang Hsu etc. in 1999 entitled “Method and System for Adaptive Video Image Resolution Enhancement” was a representative example adopting the concept. Subsequently, a digital image interpolation technique based on the image contour and image edge became a developing trend. As a result, to emphasize the image edge for facilitating human eyes' observation has become an important issue as well. For example, another American Published U.S. Pat. No. 6,175,659 filed by Chien-Hsiu Huang in 2001 entitled “Method and Apparatus for Image Scaling Using Adaptive Edge Enhancement” is an image interpolation technique focusing on image edge enhancement. On the other hand, the American Published Patent Application No. 20020126900 filed by Kim and Sang Yeon in 2002 entitled “Image Interpolation Method and Apparatus Thereof” is a processing method combining the conventional interpolation technique and the image edge orientation technique.
Nevertheless, how to design a better interpolation technique and evaluate an interpolation processing method in terms of its advantages and disadvantages has always -been a challenge to the developers of pertinent techniques. To meet the challenge, the invention provides a design utilizing self-learning ability of a neural network as a compensation for the shortcomings of interpolation techniques. In addition, the invention also designs a fuzzy system for image analysis applying the concept of human vision system. The fuzzy system combines both bilinear interpolation and neural network interpolation to work as an apparatus for image classification. The purpose of the combination is to obtain a balance between the image quality and the processing time, as well as to obtain a better image quality than the quality generated by the conventional enlarging technique.